Description:
Form 2
The Patent Act 1970
(39 of 1970)
AND
The Patent Rules 2003
COMPLETE SPECIFICATION
(Sec 10 and Rule 13)
1. TITLE OF THE INVENTION: SYSTEM AND METHOD FOR GENERATING DETERMINISTIC GROUNDED MULTI-MODAL RESPONSE TO A NATURAL LANGUAGE QUERY

2. APPLICANT (S):
(a) NAME: HYPR4 CLOUD TECH PRIVATE LIMITED
(b) NATIONALITY: INDIAN
(c) ADDRESS: 16th Floor, Orbit by Auro Reality, Unit 1610, B-Wing, Knowledge City Rd, Rai Durg, Hyderabad, Telangana 500032

Preamble
The following complete specification particularly describes the invention and the manner in which it is to be performed.

DESCRIPTION
TECHNICAL FIELD
The present disclosure generally relates to information retrieval and natural language processing systems, and more particularly to systems and methods for generating deterministic and multi-modal grounded responses to natural language queries using heterogeneous information sources.
BACKGROUND
With the rapid growth of digital information, a vast amount of data is distributed across multiple heterogeneous sources such as unstructured documents, structured databases, knowledge graphs, and vector-based embedding repositories. Conventional information retrieval systems are typically designed to operate on a single type of data source or rely on loosely integrated retrieval mechanisms, which limits their ability to comprehensively utilize diverse data modalities. As a result, existing systems often fail to effectively combine structured and unstructured data to generate coherent and accurate responses without hallucination.
Recent advancements in large language models and natural language processing techniques have enabled systems to generate fluent and contextually relevant responses to user queries. However, such systems frequently suffer from limitations including hallucinated outputs, lack of traceability, and insufficient grounding in verifiable evidence. In particular, responses generated without robust validation mechanisms may incorporate semantically incompatible or conflicting information derived from multiple sources, thereby reducing reliability and user trust.
Furthermore, existing approaches generally lack deterministic validation frameworks capable of ensuring semantic compatibility among retrieved evidence items prior to response synthesis. The absence of controlled gating mechanisms allows response generation models to incorporate partially relevant or inconsistent evidence, leading to inaccuracies and ambiguity. Additionally, current systems often do not provide adequate traceability linking generated responses to their underlying evidence, making it difficult to verify correctness or audit the reasoning process.
There is therefore a need for an improved system and method that can retrieve and integrate information from heterogeneous sources, validate semantic consistency among retrieved evidence, and generate grounded responses that are explainable, traceable, and free from semantically incompatible information or hallucination.
SUMMARY
In some aspects of the present disclosure, a system for generating a grounded response to a natural language query using heterogeneous information sources is provided.
The system includes a user device configured to receive one or more inputs representing a natural language query from a user. The system further includes a retrieval subsystem configured to obtain a plurality of evidence items corresponding to the natural language query from at least two heterogeneous information sources. The system further includes a reasoning validation subsystem configured to evaluate semantic compatibility among the plurality of evidence items based on one or more compatibility conditions. The system further includes a response generation subsystem configured to generate the grounded response using a deterministic approach. The system further includes a synthesis control gate operatively coupled between the reasoning validation subsystem and the response generation subsystem. The synthesis control gate is configured to permit generation of the grounded response only when the reasoning validation subsystem determines that the plurality of evidence items satisfy the semantic compatibility conditions. The synthesis control gate is further configured to restrict the response generation subsystem to utilize exclusively validated evidence items that satisfy the semantic compatibility conditions. The synthesis control gate is further configured to prevent generation of the grounded response when the semantic compatibility conditions are not satisfied, thereby enforcing deterministic, pre-synthesis validation-controlled response generation and preventing synthesis from semantically incompatible evidence sources. The reasoning validation subsystem is configured to control response generation by permitting synthesis only when semantic consistency conditions are satisfied, and wherein the context reconstruction subsystem, semantic identity subsystem, semantic memory subsystem, canonical query abstraction subsystem, retrieval subsystem, and reasoning validation subsystem operate as coordinated layers of a deterministic knowledge processing architecture.
In some aspects of the present disclosure, the system further includes a semantic memory subsystem that is communicatively coupled with each of a retrieval subsystem, a reasoning validation subsystem, a response generation subsystem, synthesis control gate, and configured to store entities, relationships, contextual attributes, and evidence references derived from the contextual evidence units, wherein the semantic memory subsystem stores entity nodes comprising graph relationships, vector embeddings, contextual attributes, and references to source evidence.
In some aspects of the present disclosure, the system further includes a context reconstruction subsystem configured to process source information obtained from one or more data repositories, wherein the context reconstruction subsystem is configured to segment the source information into a plurality of content blocks corresponding to structural elements including at least one of headings, paragraphs, tables, lists, or metadata fields, and to construct contextual evidence units representing semantically associated information.
In some aspects of the present disclosure, the context reconstruction subsystem is configured to extract semantic anchors from each content block, the semantic anchors comprising at least one of named entities identified using named entity recognition, ontology-defined conceptual terms identified using ontology matching, or domain-specific identifiers extracted from structured or semi-structured data. The context reconstruction subsystem is further configured to determine an anchor overlap score between two or more content blocks based on a ratio of intersecting semantic anchors to a union of semantic anchors associated with the content blocks. The context reconstruction subsystem is further configured to determine a positional proximity value representing a structural or token-based distance between the content blocks within an original source document. The context reconstruction subsystem is further configured to merge the two or more content blocks into a contextual evidence unit when (i) the anchor overlap score exceeds a predefined anchor threshold and (ii) the positional proximity value is below a predefined positional gap threshold. The context reconstruction subsystem is further configured to associate each contextual evidence unit with positional metadata including at least one of document identifiers, page numbers, token offsets, or structural coordinates corresponding to original source locations.
In some aspects of the present disclosure, the system further includes a semantic identity subsystem configured to detect entities within contextual evidence units and generate deterministic entity identifiers corresponding to the detected entities, wherein the deterministic entity identifiers are generated using a cryptographic hashing function applied to a concatenated representation that includes (i) a namespace identifier corresponding to a source modality, (ii) an entity type classification, and (iii) a normalized entity representation, wherein the namespace identifier is configured to distinguish entities originating from at least one of structured databases, unstructured documents, or ontology repositories to prevent cross-modal identity collisions. The normalized entity representation is generated by applying at least one normalization operation including case normalization, lemmatization, punctuation removal, abbreviation expansion, or canonicalization. The semantic identity subsystem is further configured to merge entity attributes and associated evidence references when identical deterministic entity identifiers are detected, and to maintain separate entity representations when namespace identifiers differ.
In some aspects of the present disclosure, the system further includes a canonical query abstraction subsystem that is configured to interpret the natural language query and transform the natural language query into a structured semantic query representation, wherein the structured semantic query representation comprises: (i) a domain concept, (ii) one or more attribute constraints, (iii) one or more relational predicates, and (iv) optionally, numerical or aggregation constraints.
In some aspects of the present disclosure, the system further includes a semantic binding registry configured to store mappings between conceptual entities of the structured semantic query representation and corresponding data source structures such that the canonical query abstraction subsystem is further configured to validate the structured semantic query representation by verifying correspondence with at least one mapping stored in the semantic binding registry prior to execution.
In some aspects of the present disclosure, the retrieval subsystem is configured to retrieve evidence items from at least two heterogeneous information sources selected from document repositories, relational databases, knowledge graphs, and vector embedding indexes. The retrieval subsystem is further configured to dynamically select a retrieval strategy based on characteristics of the structured semantic query representation, further wherein the retrieval subsystem is configured to perform at least one of (i) vector similarity search, (ii) structured query execution, (iii) graph traversal, or (iv) document evidence retrieval. The retrieval subsystem is further configured to aggregate retrieved evidence items into a unified evidence set, wherein each evidence item is associated with at least one deterministic entity identifier and a contextual evidence reference.
In some aspects of the present disclosure, the reasoning validation subsystem is configured to evaluate semantic compatibility among retrieved evidence items based on at least one of (i) shared deterministic entity identifiers, (ii) shared or hierarchically related ontology concepts using ontology subsumption relationships, or (iii) alignment of query constraints associated with the retrieved evidence items, The reasoning validation subsystem is further configured to compute a semantic overlap score using a weighted similarity function. The weighted similarity function comprises a weighted Jaccard similarity measure based on at least one of entity overlap, concept overlap, and constraint compatibility. The reasoning validation subsystem is configured to determine that the semantic compatibility is satisfied when the semantic overlap score exceeds a predefined validation threshold.
In some aspects of the present disclosure, the synthesis control gate is configured to (i) block transmission of evidence items to the response generation subsystem when the semantic compatibility conditions are not satisfied, (ii) selectively pass only validated evidence items satisfying the semantic compatibility conditions to the response generation subsystem, and (iii) enforce deterministic control over response generation by restricting input to the response generation subsystem exclusively to the validated evidence items.
In some aspects of the present disclosure, the response generation subsystem is configured to generate the grounded response using only the validated evidence items received through the synthesis control gate, and further wherein the grounded response includes references to one or more contextual evidence units associated with the validated evidence items.
In second aspect of the present disclosure, a method for generating a grounded response to a natural language query using heterogeneous information sources, is provided.
The method includes receiving, at the user device, one or more inputs representing a natural language query from a user. The method further includes processing, by the context reconstruction subsystem, source information obtained from one or more data repositories by segmenting the source information into a plurality of content blocks corresponding to structural elements including at least one of headings, paragraphs, tables, lists, or metadata fields. The method further includes extracting, by the context reconstruction subsystem, semantic anchors from each content block, the semantic anchors comprising at least one of named entities, ontology-defined conceptual terms, or domain-specific identifiers. The method further includes determining, by the context reconstruction subsystem, an anchor overlap score between two or more content blocks based on a ratio of intersecting semantic anchors to a union of semantic anchors. The method further includes determining a positional proximity value representing a structural or token-based distance between the content blocks. The method further includes merging, by a context reconstruction subsystem, the two or more content blocks into contextual evidence units when the anchor overlap score exceeds a predefined threshold and the positional proximity value is below a predefined threshold and associating each contextual evidence unit with positional metadata including at least one of document identifiers, page numbers, token offsets, or structural coordinates. The method further includes detecting, by a semantic identity subsystem, entities within the contextual evidence units and generating deterministic entity identifiers corresponding to the detected entities using a cryptographic hashing function applied to a concatenated representation comprising a namespace identifier, an entity type classification, and a normalized entity representation. The method further includes transforming, by a canonical query abstraction subsystem, the natural language query into a structured semantic query representation comprising a domain concept, one or more attribute constraints, one or more relational predicates, and optionally numerical or aggregation constraints. The method further includes validating, using a semantic binding registry, the structured semantic query representation by verifying correspondence with at least one mapping between conceptual entities and data source structures. The method further includes retrieving, by a retrieval subsystem, a plurality of evidence items corresponding to the structured semantic query representation from at least two heterogeneous information sources, wherein retrieving comprises dynamically selecting a retrieval strategy and performing at least one of vector similarity search, structured query execution, graph traversal, or document evidence retrieval, and aggregating the retrieved evidence items into a unified evidence set associated with deterministic entity identifiers and contextual evidence references. The method further includes evaluating, by a reasoning validation subsystem, semantic compatibility among the plurality of evidence items based on at least one of shared deterministic entity identifiers, shared or hierarchically related ontology concepts, or alignment of query constraints, and computing a semantic overlap score using a weighted similarity function including a weighted Jaccard similarity measure, and determining that semantic compatibility is satisfied when the semantic overlap score exceeds a predefined validation threshold. The method further includes controlling, by a synthesis control gate operatively coupled to the reasoning validation subsystem, response generation by (i) permitting generation of the grounded response only when the semantic compatibility is satisfied, (ii) restricting response generation to utilize exclusively validated evidence items, and (iii) preventing generation of the grounded response when semantic compatibility is not satisfied; The method further includes generating, by a response generation subsystem, the grounded response using only the validated evidence items, wherein the grounded response includes references to one or more contextual evidence units associated with the validated evidence items and logging at least one of validation outcomes, semantic overlap scores, or synthesis control decisions to enable deterministic, auditable, and reproducible response generation.
BRIEF DESCRIPTION OF DRAWINGS
The above and still further features and advantages of aspects of the present disclosure become apparent upon consideration of the following detailed description of aspects thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
Figure 1 illustrates a system for generating a grounded response to a natural language query using heterogeneous information sources, in accordance with an aspect of the present disclosure; and
Figure 2 illustrates a flowchart that depicts a method for generating a grounded response to a natural language query using heterogeneous information sources, in accordance with an aspect of the present disclosure.
To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures.
DETAILED DESCRIPTION
The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.
The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
Spatially relative terms, such as “top,” “bottom,” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It is to be understood that the spatially relative terms are intended to encompass different orientations of the structure in use or operation in addition to the orientation depicted in the figures.
The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventor/inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies.
As discussed before, there a need for an improved system and method that can retrieve and integrate information from heterogeneous sources, validate semantic consistency among retrieved evidence, and generate grounded responses that are explainable, traceable, and free from semantically incompatible information. The present disclosure, therefore, provides a system and method for generating a grounded response to a natural language query using heterogeneous information sources, wherein the system is configured to retrieve evidence items from multiple data modalities, validate semantic compatibility among the retrieved evidence using deterministic validation mechanisms, and generate explainable and traceable responses based exclusively on validated evidence. The system further enables controlled and deterministic response generation through a synthesis control mechanism that prevents incorporation of semantically incompatible information, while supporting hybrid retrieval strategies across structured, unstructured, and graph-based data sources to improve accuracy, reliability, and reproducibility of the generated responses.
Figure 1 illustrates a block diagram of a system 100 for generating a grounded response to a natural language query using heterogeneous information sources, in accordance with an aspect of the present disclosure.
The system 100 may include a user device 102 and a server 103. The user device 102 and a server 104 may be coupled with each other by way of a communication network 105. The communication network 105 may include, but is not limited to, one or more of a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a global network such as the Internet, an intranet, an extranet, or any combination thereof. The communication network 105 may be implemented using one or more wired communication technologies including Ethernet, optical fiber, coaxial cable, or digital subscriber line (DSL), and/or one or more wireless communication technologies including Wi-Fi (IEEE 802.11 standards), Bluetooth, Zigbee, near-field communication (NFC), radio frequency (RF) communication, infrared (IR) communication, satellite communication, or cellular communication technologies such as second generation (2G), third generation (3G), fourth generation (4G LTE), fifth generation (5G), or future-generation wireless networks. In some embodiments, the communication network 105 may support packet-switched and/or circuit-switched communication and may operate using one or more communication protocols, including but not limited to Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Message Queuing Telemetry Transport (MQTT), WebSocket protocols, or other suitable communication protocols. The communication network 105 may further include one or more intermediary devices such as routers, switches, gateways, access points, base stations, or edge computing nodes configured to facilitate data transmission between system components. In certain implementations, the communication network may support secure communication using encryption techniques, authentication protocols, and secure tunneling mechanisms such as Virtual Private Networks (VPNs) or Transport Layer Security (TLS).
The user device 102 may be configured to receive one or more inputs representing a natural language query from a user. In some aspects of the present disclosure, the user device 102 may be further configured to preprocess the natural language query using at least one of token normalization, language detection, or query intent classification prior to transmission to the server 103. In some aspects of the present disclosure, the user device 102 may be configured to capture contextual metadata associated with the natural language query including at least one of user session identifiers, temporal context, or application context, and transmit the contextual metadata along with the query. In some aspects of the present disclosure, the user device 102 may be further configured to present the grounded response along with traceability metadata including references to contextual evidence units and deterministic entity identifiers. In some aspects of the present disclosure, the user device 102 may be configured to enable interactive exploration of evidence references, wherein a user may selectively view underlying contextual evidence units associated with portions of the grounded response. In some aspects of the present disclosure, the user device 102 may be configured to transmit feedback signals corresponding to user validation or correction of the grounded response to the server 103 for improving subsequent processing. Examples of the user device 102 may be selected from at least one of a smartphone, a tablet computing device, a laptop computer, a desktop computer, a wearable device, a smart assistant device, or any other computing device configured to receive user inputs and communicate with the server 103.
The server 103 may include a retrieval sub-system 104, a reasoning validation subsystem 106, a response generation subsystem 108, and a synthesis control gate 110. The retrieval sub-system 104, the reasoning validation subsystem 106, the response generation subsystem 108, and the synthesis control gate 110 may be communicatively coupled with each other by way of a communication bus 111. Examples of the communication bus 111 may be selected from at least one of a wired communication interface, a wireless communication interface, an internal system bus, a network communication protocol, or an inter-process communication mechanism. Examples of the server 103 may be selected from at least one of a cloud server, an application server, a distributed computing system, a virtual machine instance, a containerized microservices architecture, or a high-performance computing cluster.
The retrieval subsystem 104 may be configured to obtain a plurality of evidence items corresponding to the natural language query from at least two heterogeneous information sources. The retrieval subsystem 104 may be configured to retrieve evidence items from at least two heterogeneous information sources selected from document repositories, relational databases, knowledge graphs, and vector embedding indexes. In some aspects of the present disclosure, the retrieval subsystem 104 may further configured to dynamically select a retrieval strategy based on characteristics of the structured semantic query representation. In some aspects of the present disclosure, the retrieval subsystem 104 may be configured to perform at least one of: (i) vector similarity search, (ii) structured query execution, (iii) graph traversal, or (iv) document evidence retrieval. In some aspects of the present disclosure, the retrieval subsystem 104 may further be configured to aggregate retrieved evidence items into a unified evidence set. In some aspects of the present disclosure, each evidence item may be associated with at least one deterministic entity identifier and a contextual evidence reference. In some aspects of the present disclosure, the retrieval subsystem 104 may be further configured to operate as a hybrid retrieval orchestration engine that dynamically prioritizes retrieval modalities based on structural features of the structured semantic query representation, wherein queries containing relational predicates trigger graph traversal, queries containing attribute constraints trigger structured database queries, and queries containing semantic similarity requirements trigger vector similarity search. In some aspects of the present disclosure, the retrieval subsystem 104 may be further configured to assign modality-specific confidence weights to retrieved evidence items based on source type, retrieval method, and query alignment, and to utilize the confidence weights during aggregation into the unified evidence set. In some aspects of the present disclosure, the retrieval subsystem 104 may be further configured to perform iterative retrieval refinement, wherein an initial set of evidence items is used to reformulate or expand the structured semantic query representation for subsequent retrieval cycles. In some aspects of the present disclosure, the retrieval subsystem 104 may be configured to utilize entity-centric retrieval, wherein retrieval operations are anchored around deterministic entity identifiers stored in the semantic memory subsystem 114. In some aspects of the present disclosure, the retrieval subsystem 104 may be further configured to apply cross-modal ranking, wherein evidence items retrieved from different modalities are ranked using a unified scoring function based on semantic relevance and constraint alignment. In some aspects of the present disclosure, the retrieval subsystem 104 may be configured to filter retrieved evidence items based on minimum confidence thresholds derived from modality-specific confidence weights. In some aspects of the present disclosure, the retrieval subsystem 104 may be further configured to perform context expansion, wherein additional evidence items related to initially retrieved entities are obtained from the semantic memory subsystem 114 or associated data sources.
The reasoning validation subsystem 106 may be configured to evaluate semantic compatibility among the plurality of evidence items based on one or more compatibility conditions. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be configured to evaluate semantic compatibility among retrieved evidence items based on at least one of (i) shared deterministic entity identifiers, (ii) shared or hierarchically related ontology concepts using ontology subsumption relationships, or (iii) alignment of query constraints associated with the retrieved evidence items. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be configured to compute a semantic overlap score using a weighted similarity function. In some aspects of the present disclosure, the weighted similarity function comprises a weighted Jaccard similarity measure based on at least one of entity overlap, concept overlap, and constraint compatibility. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be configured to determine that the semantic compatibility is satisfied when the semantic overlap score exceeds a predefined validation threshold. In some aspects of the present disclosure, the response generation subsystem 106 may further be configured to generate the grounded response using only the validated evidence items received through the synthesis control gate, and further wherein the grounded response includes references to one or more contextual evidence units associated with the validated evidence items. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be further configured to perform pairwise and group-wise compatibility evaluation among retrieved evidence items, wherein compatibility is assessed across combinations of two or more evidence items to ensure global semantic consistency within the unified evidence set. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be further configured to evaluate constraint alignment consistency, wherein attribute filters and relational predicates associated with different evidence items are compared to detect logical conflicts or inconsistencies. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be further configured to compute multi-level semantic validation, wherein validation is performed at entity-level, concept-level, and constraint-level granularity. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be configured to assign penalty scores to evidence items exhibiting partial incompatibility, thereby influencing overall semantic overlap scoring. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be further configured to identify and exclude outlier evidence items that significantly deviate from the majority semantic cluster within the unified evidence set. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be configured to perform temporal consistency validation, wherein temporal attributes associated with evidence items are evaluated for consistency. In some aspects of the present disclosure, the reasoning validation subsystem 106 may be further configured to generate a validation explanation output indicating reasons for acceptance or rejection of evidence items. Examples of the reasoning validation subsystem 106 may be implemented using at least one of a processor-based computing device, a server system, a graphics processing unit (GPU)-enabled computing system, a field-programmable gate array (FPGA)-based processing unit, or an application-specific integrated circuit (ASIC)-based validation engine configured to execute semantic reasoning and validation operations.
The response generation subsystem 108 may be configured to generate the grounded response. In some aspects of the present disclosure, the response generation subsystem 108 may be further configured to include traceability metadata in the grounded response, wherein the metadata comprises references to deterministic entity identifiers, contextual evidence unit identifiers, and source location attributes. In some aspects of the present disclosure, the response generation subsystem 108 may be further configured to generate evidence-attributed responses, wherein each portion of the generated response is associated with one or more supporting evidence items from the validated evidence set. In some aspects of the present disclosure, the response generation subsystem 108 may be configured to generate multi-part structured responses including summaries, detailed explanations, and evidence citations. In some aspects of the present disclosure, the response generation subsystem 108 may be further configured to generate confidence scores associated with the grounded response based on validation metrics. In some aspects of the present disclosure, the response generation subsystem 108 may be configured to adapt the format of the grounded response based on output modality including text, tabular representation, or structured data formats. In some aspects of the present disclosure, the response generation subsystem 108 may be further configured to enforce evidence coverage constraints, wherein all critical statements in the response are supported by at least one validated evidence item. In some aspects of the present disclosure, the response generation subsystem 108 may be configured to generate explainable reasoning chains linking the natural language query to the supporting evidence items. Examples of the response generation subsystem 108 may be implemented using at least one of a high-performance computing server, a processor-based computing system, a GPU-accelerated inference device, a neural processing unit (NPU)-based system, or a distributed computing cluster configured to generate natural language responses.
The synthesis control gate 110 may be operatively coupled between the reasoning validation subsystem 106 and the response generation subsystem 110. The synthesis control gate 110 may be configured to permit generation of the grounded response only when the reasoning validation subsystem 106 determines that the plurality of evidence items satisfy the semantic compatibility conditions. The synthesis control gate 110 may further be configured to restrict the response generation subsystem 108 to utilize exclusively validated evidence items that satisfy the semantic compatibility conditions. The synthesis control gate 110 may further be configured to prevent generation of the grounded response when the semantic compatibility conditions are not satisfied, thereby enforcing deterministic, pre-synthesis validation-controlled response generation and preventing synthesis from semantically incompatible evidence sources. In some aspects of the present disclosure, the synthesis control gate 110 may be configured to block transmission of evidence items to the response generation subsystem 106 when the semantic compatibility conditions are not satisfied. In some aspects of the present disclosure, the synthesis control gate 110 may further be configured to selectively pass only validated evidence items satisfying the semantic compatibility conditions to the response generation subsystem 106. In some aspects of the present disclosure, the synthesis control gate 110 may be configured to enforce deterministic control over response generation by restricting input to the response generation subsystem exclusively to the validated evidence items. In some aspects of the present disclosure, the synthesis control gate 110 may be further configured to generate and store audit logs corresponding to blocked synthesis operations, wherein the audit logs include identifiers of rejected evidence items, computed semantic overlap scores, and reasons for incompatibility. In some aspects of the present disclosure, the synthesis control gate 110 may be further configured to implement LLM input gating, wherein input prompts to the response generation subsystem 108 are constructed exclusively from validated evidence items and exclude any non-validated or partially compatible evidence. In some aspects of the present disclosure, the synthesis control gate 110 may be further configured to enforce deterministic execution paths, wherein identical natural language queries processed under identical system states produce reproducible outputs by restricting stochastic variations in evidence selection and synthesis. In some aspects of the present disclosure, the synthesis control gate 110 may be further configured to implement graded gating mechanisms, wherein partial response generation is permitted when a subset of evidence items satisfies relaxed compatibility thresholds. In some aspects of the present disclosure, the synthesis control gate 110 may be configured to trigger fallback mechanisms, wherein alternative retrieval or validation processes are initiated when semantic compatibility conditions are not satisfied. In some aspects of the present disclosure, the synthesis control gate 110 may be further configured to enforce policy-based constraints, wherein predefined rules govern allowable evidence combinations for response generation. In some aspects of the present disclosure, the synthesis control gate 110 may be configured to maintain execution logs capturing the sequence of subsystem operations and gating decisions. In some aspects of the present disclosure, the synthesis control gate 110 may be further configured to regulate resource utilization, wherein response generation is permitted only when computational constraints are satisfied. Examples of the synthesis control gate 110 may be implemented using at least one of a processor-executed control module, a hardware-based logic controller, an FPGA-based gating unit, an ASIC-based control circuit, or a middleware execution controller implemented on a computing device.
In some aspects of the present disclosure, the reasoning validation subsystem 106 may be configured to control response generation by permitting synthesis only when semantic consistency conditions are satisfied, and the context reconstruction subsystem 112, semantic identity subsystem 116, semantic memory subsystem 114, canonical query abstraction subsystem 118, retrieval subsystem 102, and reasoning validation subsystem 106 operate as coordinated layers of a deterministic knowledge processing architecture
The server 103 may further include a context reconstruction subsystem 112, a semantic identity subsystem 116, a canonical query abstraction subsystem 118, and a semantic binding registry 120. The context reconstruction subsystem 112, the semantic identity subsystem 116, the canonical query abstraction subsystem 118, and the semantic binding registry 120 may be communicatively coupled with each other and further with the retrieval sub-system 104, the reasoning validation subsystem 106, the response generation subsystem 108, and the synthesis control gate 110 by way of the communication bus 111.
The context reconstruction subsystem 112 may be configured to process source information obtained from one or more data repositories. The context reconstruction subsystem 112 may further be configured to segment the source information into a plurality of content blocks corresponding to structural elements including at least one of headings, paragraphs, tables, lists, or metadata fields, and to construct contextual evidence units representing semantically associated information. In some aspects of the present disclosure, the context reconstruction subsystem 112 may further be configured to extract semantic anchors from each content block, the semantic anchors comprising at least one of named entities identified using named entity recognition, ontology-defined conceptual terms identified using ontology matching, or domain-specific identifiers extracted from structured or semi-structured data. In some aspects of the present disclosure, the context reconstruction subsystem 112 may further be configured to determine an anchor overlap score between two or more content blocks based on a ratio of intersecting semantic anchors to a union of semantic anchors associated with the content blocks. In some aspects of the present disclosure, the context reconstruction subsystem 112 may further be configured to determine a positional proximity value representing a structural or token-based distance between the content blocks within an original source document. In some aspects of the present disclosure, the context reconstruction subsystem 112 may further be configured to merge the two or more content blocks into a contextual evidence unit when (i) the anchor overlap score exceeds a predefined anchor threshold and (ii) the positional proximity value is below a predefined positional gap threshold. In some aspects of the present disclosure, the context reconstruction subsystem 112 may further be configured to associate each contextual evidence unit with positional metadata including at least one of document identifiers, page numbers, token offsets, or structural coordinates corresponding to original source locations. In some aspects of the present disclosure, the context reconstruction subsystem 112 may further be configured to detect entities within contextual evidence units and generate deterministic entity identifiers corresponding to the detected entities, wherein the deterministic entity identifiers are generated using a cryptographic hashing function applied to a concatenated representation comprising: (i) a namespace identifier corresponding to a source modality, (ii) an entity type classification, and (iii) a normalized entity representation, wherein the namespace identifier is configured to distinguish entities originating from at least one of structured databases, unstructured documents, or ontology repositories to prevent cross-modal identity collisions, wherein the normalized entity representation is generated by applying at least one normalization operation including case normalization, lemmatization, punctuation removal, abbreviation expansion, or canonicalization. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be further configured to utilize a bridging instruction for merging content blocks, wherein the bridging algorithm concatenates semantically related blocks while preserving positional metadata and structural ordering. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be further configured to enforce maximum positional gap constraints, wherein merging of content blocks is restricted when the structural or token-based distance exceeds a predefined threshold. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be further configured to assign importance weights to content blocks based on structural roles such as headings, tables, or metadata fields. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be configured to perform cross-document evidence linking, wherein content blocks from different documents are merged into a single contextual evidence unit based on shared anchors. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be further configured to normalize structural variations across documents to enable consistent segmentation. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be configured to maintain hierarchical relationships among contextual evidence units. In some aspects of the present disclosure, the context reconstruction subsystem 112 may be further configured to generate contextual embeddings for each evidence unit to support downstream retrieval. Examples of the context reconstruction subsystem 112 may be implemented using at least one of a processor-based document processing system, a data processing server, a GPU-enabled data transformation system, an FPGA-based parsing unit, or a distributed data processing platform configured to perform content segmentation and reconstruction.
In some aspects of the present disclosure, the system 100 may further include a semantic memory subsystem 114 communicatively coupled via the communication bus 111, wherein the semantic memory subsystem 114 may be configured to persistently store entity nodes, relationships, contextual attributes, embedding representations, and references to contextual evidence units generated by the context reconstruction subsystem 112. In some aspects of the present disclosure, the semantic memory subsystem 114 may be configured to maintain identity embedding vectors and contextual profile vectors for each deterministic entity identifier generated by the semantic identity subsystem 116, thereby enabling hybrid retrieval across symbolic and vector-based representations. In some aspects of the present disclosure, the semantic memory subsystem 114 may be configured to maintain graph-based relationships between entity nodes, wherein the relationships include at least one of hierarchical relationships, associative relationships, or domain-specific predicates derived from structured or unstructured data sources. In some aspects of the present disclosure, the semantic memory subsystem 114 may be further configured to support incremental updates, wherein new evidence units and entity nodes are continuously integrated. In some aspects of the present disclosure, the semantic memory subsystem 114 may be configured to maintain versioned entity representations to track evolution of entity attributes over time. In some aspects of the present disclosure, the semantic memory subsystem 114 may be further configured to support query-time indexing, enabling efficient retrieval of relevant entities and evidence units. In some aspects of the present disclosure, the semantic memory subsystem 114 may be configured to maintain cross-modal linking structures connecting vector embeddings, graph nodes, and structured attributes. In some aspects of the present disclosure, the semantic memory subsystem 114 may be further configured to provide retrieval hints to the retrieval subsystem 104 based on historical query patterns. Examples of the semantic memory subsystem 114 may be implemented using at least one of a database server, a memory storage device, a distributed storage cluster, a high-speed in-memory computing system, or a hybrid storage infrastructure comprising solid-state drives (SSD) and volatile memory.
In some aspects of the present disclosure, the semantic memory subsystem 114 may be communicatively coupled with each of a retrieval subsystem 104, a reasoning validation subsystem 106, a response generation subsystem 108, synthesis control gate 110, and configured to store entities, relationships, contextual attributes, and evidence references derived from the contextual evidence units. The semantic memory subsystem 114 may store entity nodes comprising graph relationships, vector embeddings, contextual attributes, and references to source evidence.
The semantic identity subsystem 116 may be configured to merge entity attributes and associated evidence references when identical deterministic entity identifiers are detected, and to maintain separate entity representations when namespace identifiers differ. In some aspects of the present disclosure, the semantic identity subsystem 116 may be further configured to prevent cross-modal identity merging, wherein entities having identical normalized representations, but different namespace identifiers are maintained as distinct entity nodes. In some aspects of the present disclosure, the semantic identity subsystem 116 may be further configured to perform entity disambiguation using contextual evidence. In some aspects of the present disclosure, the semantic identity subsystem 116 may be configured to generate composite entity identifiers for entities derived from multiple sources. In some aspects of the present disclosure, the semantic identity subsystem 116 may be further configured to maintain alias mappings for entities having multiple textual representations. In some aspects of the present disclosure, the semantic identity subsystem 116 may be configured to detect conflicting entity attributes and preserve them as separate contextual variants. In some aspects of the present disclosure, the semantic identity subsystem 116 may be further configured to validate entity identity consistency across processing stages. Examples of the semantic identity subsystem 116 may be implemented using at least one of a processor-based identity resolution server, a cryptographic processing unit, an FPGA-based hashing module, an ASIC-based identity generation circuit, or a computing system configured to perform entity normalization and identification.
The canonical query abstraction subsystem 118 may be configured to interpret the natural language query and transform the natural language query into a structured semantic query representation. In some aspects of the present disclosure, the structured semantic query representation may include (i) a domain concept, (ii) one or more attribute constraints, (iii) one or more relational predicates, and (iv) optionally, numerical or aggregation constraints. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be further configured to perform query decomposition, wherein a complex natural language query is decomposed into multiple sub-queries corresponding to distinct domain concepts, relational predicates, or aggregation constraints. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be further configured to enforce schema-aligned constraint validation, wherein each attribute constraint and relational predicate in the structured semantic query representation is verified against permissible schema definitions stored in the semantic binding registry 120 prior to execution. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be further configured to perform semantic normalization of query terms using ontology mappings. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be configured to detect implicit constraints within the natural language query. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be further configured to generate multiple candidate query representations for ambiguous queries. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be configured to prioritize query constraints based on relevance. In some aspects of the present disclosure, the canonical query abstraction subsystem 118 may be further configured to map query components to corresponding entity identifiers stored in the semantic memory subsystem 114. Examples of the canonical query abstraction subsystem 118 may be implemented using at least one of a processor-based query processing system, a natural language processing server, a GPU-accelerated computation device, an FPGA-based transformation unit, or a distributed query processing platform.
The semantic binding registry 120 may be configured to store mappings between conceptual entities of the structured semantic query representation and corresponding data source structures such that the canonical query abstraction subsystem 118 is further configured to validate the structured semantic query representation by verifying correspondence with at least one mapping stored in the semantic binding registry prior to execution. In some aspects of the present disclosure, the semantic binding registry 120 may be further configured to store multi-modal binding mappings, wherein a single conceptual entity is mapped to multiple data source structures across heterogeneous modalities including relational schemas, graph schemas, and document indices. In some aspects of the present disclosure, the system 100 may be further configured to operate as a deterministic knowledge processing pipeline, wherein each subsystem operates in a fixed sequence and produces intermediate outputs that are uniquely determined by input data and system configuration parameters. In some aspects of the present disclosure, the system 100 may be further configured to reduce hallucinated reasoning by preventing synthesis from evidence items that do not share at least one of deterministic entity identifiers, ontology-aligned concepts, or compatible query constraints. In some aspects of the present disclosure, the system 100 may be further configured to improve retrieval recall across heterogeneous modalities by aggregating evidence from multiple coordinated retrieval strategies prior to validation. In some aspects of the present disclosure, the system 100 may be further configured to generate a cryptographic execution state identifier corresponding to each query processing instance, wherein the execution state identifier is derived from at least one of retrieved evidence identifiers, deterministic entity identifiers, and validation outcomes to enable reproducibility and auditability. In some aspects of the present disclosure, the semantic binding registry 120 may be further configured to maintain bidirectional mappings between conceptual entities and data source structures. In some aspects of the present disclosure, the semantic binding registry 120 may be configured to support dynamic updates based on schema evolution. In some aspects of the present disclosure, the semantic binding registry 120 may be further configured to validate relationship constraints between mapped entities. In some aspects of the present disclosure, the semantic binding registry 120 may be configured to store confidence scores associated with each mapping. In some aspects of the present disclosure, the semantic binding registry 120 may be further configured to enable cross-domain mappings between different ontologies. Examples of the semantic binding registry 120 may be implemented using at least one of a database management server, a metadata storage system, a distributed registry server, a cloud-based storage infrastructure, or a persistent storage device configured to store mapping information.
The server 103 further includes a network interface 122, an input/output interface 126, and a storage unit 124.
The network interface 122 is configured to enable communication between the server 103 and the user device 102 through the communication network 105. The network interface 122 may support a wide range of wired and wireless communication technologies, including Ethernet, optical fiber communication, Wi-Fi, cellular communication protocols, and other suitable networking standards. The network interface 122 may further be configured to handle data transmission and reception, packet routing, protocol conversion, and secure communication using encryption mechanisms and authentication protocols to ensure integrity and confidentiality of data exchanged between system components.
The input/output interface 126 is configured to facilitate interaction between the server 103 and one or more external devices. The input/output interface 126 may support connection with input devices such as keyboards, pointing devices, or administrative consoles, and output devices such as display units, monitoring dashboards, or logging systems. The input/output interface 126 may further enable integration with auxiliary computing systems, debugging tools, and system management platforms, thereby supporting configuration, monitoring, maintenance, and control of operations performed by the server 103.
The storage unit 124 is configured to store data and instructions required for operation of the system 100. The storage unit 124 may include one or more memory components such as volatile memory including random-access memory, and non-volatile memory including read-only memory, solid-state drives, magnetic disks, or other persistent storage media. The storage unit 124 may store executable program instructions corresponding to the retrieval subsystem 104, the reasoning validation subsystem 106, the response generation subsystem 108, the synthesis control gate 110, the context reconstruction subsystem 112, the semantic memory subsystem 114, the semantic identity subsystem 116, the canonical query abstraction subsystem 118, and the semantic binding registry 120. The storage unit 124 may further store intermediate processing data, structured semantic query representations, retrieved evidence items, contextual evidence units, deterministic entity identifiers, mapping data, configuration parameters, audit logs, and historical query data. Additionally, the storage unit 124 may be configured to support high-speed data access, data indexing, caching mechanisms, and backup or replication strategies to ensure reliability, scalability, and efficient retrieval of stored information during system operation.
In operation, the system 100 provides a deterministic and validation-controlled pipeline for generating a grounded response to a natural language query using heterogeneous information sources. The process begins with the user device 102 receiving a natural language query from a user and optionally preprocessing the query using token normalization, language detection, and query intent classification. The user device 102 may also capture contextual metadata including user session identifiers, temporal context, or application context, and transmit the processed query along with the contextual metadata to the server 103 via the communication network 105. Upon receiving the query, the canonical query abstraction subsystem 118 transforms the natural language query into a structured semantic query representation comprising a domain concept, attribute constraints, relational predicates, and optional aggregation constraints. The structured semantic query representation is validated using mappings stored in the semantic binding registry 120 to ensure schema consistency prior to execution. Concurrently, the context reconstruction subsystem 112 processes source information obtained from one or more data repositories by segmenting the source information into content blocks and constructing contextual evidence units based on semantic anchors, anchor overlap scores, and positional proximity values. The context reconstruction subsystem 112 further generates deterministic entity identifiers and contextual embeddings associated with the contextual evidence units. The semantic identity subsystem 116 unifies or distinguishes entity representations by merging entity attributes corresponding to identical deterministic entity identifiers and preserving distinct identities where namespace identifiers differ, thereby ensuring consistent entity representation across heterogeneous data sources. The semantic memory subsystem 114 stores entity nodes, relationships, embeddings, and contextual evidence units, and provides retrieval hints and entity-centric indexing to support efficient hybrid retrieval operations. The retrieval subsystem 104 retrieves a plurality of evidence items corresponding to the structured semantic query representation from at least two heterogeneous information sources by dynamically selecting one or more retrieval strategies including vector similarity search, structured query execution, graph traversal, and document evidence retrieval. The retrieval subsystem 104 further performs hybrid retrieval orchestration, iterative retrieval refinement, cross-modal ranking, and confidence-based filtering to generate a unified evidence set. The reasoning validation subsystem 106 evaluates semantic compatibility among the retrieved evidence items by computing a semantic overlap score using a weighted similarity function and assessing compatibility based on deterministic entity identifiers, ontology-aligned concepts, and constraint alignment. The reasoning validation subsystem 106 further performs multi-level validation, pairwise and group-wise compatibility analysis, temporal consistency validation, and outlier detection to ensure global semantic consistency within the unified evidence set. The synthesis control gate 110 regulates the flow of evidence items to the response generation subsystem 108 by permitting only validated evidence items that satisfy predefined semantic compatibility conditions. The synthesis control gate 110 further enforces deterministic execution, implements input gating, generates audit logs, and optionally triggers fallback mechanisms or graded gating when compatibility conditions are partially satisfied. The response generation subsystem 108 generates a grounded response using exclusively the validated evidence items received through the synthesis control gate 110. The grounded response includes evidence-attributed content along with traceability metadata, confidence scores, structured outputs, and explainable reasoning chains linking the query to the supporting evidence. The generated grounded response is transmitted back to the user device 102 via the communication network 105, where it is presented along with traceability metadata and enables interactive exploration of underlying contextual evidence units. The user device 102 may further transmit feedback signals to the server 103 for iterative system improvement. The system 100 may also generate a cryptographic execution state identifier corresponding to the query processing instance, thereby enabling reproducibility, traceability, and auditability of the grounded response generation process. Overall, the system 100 ensures that response generation is strictly controlled by validated, semantically compatible evidence, thereby reducing hallucinated outputs, improving reliability, and enabling explainable and deterministic knowledge synthesis across heterogeneous information sources.
In an exemplary scenario, a user may submit a natural language query through the user device 102 seeking information that requires aggregation from multiple heterogeneous information sources. The user device 102 may preprocess the query and transmit the query along with contextual metadata to the server 103 via the communication network 105. Upon receiving the query, the canonical query abstraction subsystem 118 transforms the query into a structured semantic query representation comprising domain concepts, attribute constraints, and relational predicates, and validates the representation using mappings stored in the semantic binding registry 120. Simultaneously, the context reconstruction subsystem 112 processes source data from multiple repositories by segmenting the data into content blocks and constructing contextual evidence units based on semantic anchors, anchor overlap, and positional proximity. Deterministic entity identifiers and contextual embeddings are generated for the contextual evidence units, and the semantic identity subsystem 116 ensures consistent entity representation by merging or separating entities based on identifier matching and namespace distinctions. The processed entities, relationships, and evidence units are stored in the semantic memory subsystem 114 to support efficient retrieval. The retrieval subsystem 104 then retrieves evidence items corresponding to the structured semantic query representation from multiple heterogeneous sources such as document repositories, databases, and knowledge graphs by dynamically selecting appropriate retrieval strategies. The retrieved evidence items are aggregated into a unified evidence set and ranked based on semantic relevance and confidence scores. The reasoning validation subsystem 106 evaluates the semantic compatibility of the retrieved evidence items by computing a semantic overlap score and analyzing entity alignment, ontology relationships, and constraint consistency. Evidence items that do not meet the predefined compatibility threshold or that exhibit inconsistencies are filtered out to ensure that only semantically coherent evidence is retained. The synthesis control gate 110 then regulates the flow of validated evidence items to the response generation subsystem 108 by enforcing strict validation conditions. If the evidence items satisfy the compatibility criteria, the synthesis control gate 110 permits further processing; otherwise, it may block response generation or trigger alternative retrieval or validation processes. The response generation subsystem 108 generates a grounded response using only the validated evidence items, ensuring that each portion of the response is supported by corresponding evidence. The response may include traceability metadata, confidence scores, and structured explanations linking the query to the supporting evidence. The generated response is transmitted back to the user device 102, where it is presented to the user along with options to explore underlying evidence and provide feedback.
In another exemplary scenario, a user may submit a complex natural language query through the user device 102 that includes multiple implicit constraints, relational dependencies, and ambiguous terms. The user device 102 preprocesses the query and transmits it along with contextual metadata to the server 103 via the communication network 105. Upon receipt, the canonical query abstraction subsystem 118 decomposes the query into multiple sub-queries corresponding to distinct domain concepts and relational predicates, generates multiple candidate structured semantic query representations for ambiguous components, and validates each representation using the semantic binding registry 120. The context reconstruction subsystem 112 processes heterogeneous source data, including documents and structured repositories, by segmenting content into blocks and performing cross-document evidence linking to construct enriched contextual evidence units. The subsystem further assigns importance weights to content blocks based on structural roles and generates contextual embeddings to support downstream retrieval. The semantic identity subsystem 116 performs entity disambiguation using contextual evidence and maintains alias mappings to resolve multiple representations of the same entity while preserving conflicting attributes as separate contextual variants where necessary. The semantic memory subsystem 114 updates its stored entity nodes and relationships incrementally and provides retrieval hints based on historical query patterns. The retrieval subsystem 104 performs iterative retrieval refinement by initially retrieving a set of evidence items and subsequently expanding the query using entity-centric retrieval and context expansion techniques. Additional evidence items related to initially identified entities are retrieved from the semantic memory subsystem 114 and other data sources. The subsystem applies cross-modal ranking and filters evidence items based on confidence thresholds to produce a refined unified evidence set. The reasoning validation subsystem 106 conducts multi-level semantic validation across entity-level, concept-level, and constraint-level dimensions. It evaluates temporal consistency among evidence items and assigns penalty scores to partially incompatible items while identifying and excluding outliers that deviate from the dominant semantic cluster. The subsystem also generates validation explanation outputs indicating reasons for acceptance or rejection of specific evidence items. The synthesis control gate 110 applies graded gating mechanisms by allowing partial response generation when a subset of evidence items satisfies relaxed compatibility thresholds. In cases where compatibility conditions are not fully satisfied, the synthesis control gate 110 may trigger fallback mechanisms to re-initiate retrieval or validation processes. The gate further enforces policy-based constraints and monitors resource utilization to ensure controlled execution. The response generation subsystem 108 generates a multi-part structured grounded response that includes summaries, detailed explanations, and evidence citations. It adapts the response format based on output requirements and ensures that all critical statements are supported by validated evidence items. The subsystem also generates confidence scores and explainable reasoning chains linking the query to supporting evidence. The generated response is transmitted back to the user device 102, where the user can interactively explore supporting evidence and provide feedback. The system 100 further generates a cryptographic execution state identifier for the processing instance, enabling reproducibility and auditability.
In another exemplary scenario, a user may submit a time-sensitive natural language query through the user device 102 that requires consistency across temporal attributes from multiple heterogeneous sources. The user device 102 preprocesses the query and transmits it along with contextual metadata to the server 103 via the communication network 105. Upon receipt, the canonical query abstraction subsystem 118 interprets the query, detects implicit temporal constraints, and generates a structured semantic query representation incorporating domain concepts, temporal filters, and relational predicates, which is validated using the semantic binding registry 120. The context reconstruction subsystem 112 processes incoming data streams and historical repositories by segmenting content into blocks and constructing contextual evidence units while preserving temporal metadata such as timestamps, document versions, and update sequences. The subsystem ensures that contextual evidence units reflect temporal ordering and may restrict merging of content blocks where temporal gaps exceed predefined thresholds. The semantic identity subsystem 116 maintains versioned entity representations to capture changes in entity attributes over time and ensures that temporal variations are preserved as distinct contextual variants where necessary. The semantic memory subsystem 114 stores these temporally enriched entity nodes and supports query-time indexing to enable efficient retrieval of time-relevant evidence. The retrieval subsystem 104 dynamically selects retrieval strategies based on the temporal nature of the query and retrieves evidence items from heterogeneous sources, including time-indexed databases and document repositories. The subsystem applies context expansion to include temporally adjacent evidence and filters retrieved items based on recency and relevance, producing a unified evidence set enriched with temporal attributes. The reasoning validation subsystem 106 evaluates semantic compatibility with an emphasis on temporal consistency by verifying alignment of timestamps, sequence of events, and validity intervals associated with evidence items. Evidence items that exhibit temporal conflicts or inconsistencies are penalized or excluded, and only those satisfying temporal coherence and semantic compatibility thresholds are retained. The synthesis control gate 110 enforces strict validation by permitting only temporally consistent and semantically compatible evidence items to pass to the response generation subsystem 108. In cases where temporal inconsistencies are detected, the synthesis control gate 110 may trigger fallback retrieval processes to obtain updated or alternative evidence. The gate further logs validation outcomes and ensures deterministic execution. The response generation subsystem 108 generates a grounded response that incorporates temporally aligned information, presenting the response in a structured format that may include timelines, event sequences, or time-based summaries. The response includes traceability metadata linking each statement to its corresponding evidence and temporal attributes, along with confidence scores reflecting validation strength. The generated response is transmitted back to the user device 102, where it is presented along with options to explore time-specific evidence and verify source details.
Figure 2 illustrates a flowchart that depicts a method 200 for generating a grounded response to a natural language query using heterogeneous information sources, in accordance with an aspect of the present disclosure.
At step 202, the system 100 may be configured to receive, at the user device 102, one or more inputs representing a natural language query from the user.
At step 204, the system 100 may be configured to process, by the context reconstruction subsystem 112, source information obtained from one or more data repositories by segmenting the source information into a plurality of content blocks corresponding to structural elements including at least one of headings, paragraphs, tables, lists, or metadata fields.
At step 206, the system 100 may be configured to extract, by the context reconstruction subsystem 112, semantic anchors from each content block, the semantic anchors comprising at least one of named entities, ontology-defined conceptual terms, or domain-specific identifiers
At step 208, the system 100 may be configured to determine, by the context reconstruction subsystem 112, an anchor overlap score between two or more content blocks based on a ratio of intersecting semantic anchors to a union of semantic anchors.
At step 210, the system 100 may be configured to determine a positional proximity value representing a structural or token-based distance between the content blocks.
At step 212, the system 100 may be configured to merge, by a context reconstruction subsystem 112, the two or more content blocks into contextual evidence units when the anchor overlap score exceeds a predefined threshold and the positional proximity value is below a predefined threshold and associating each contextual evidence unit with positional metadata including at least one of document identifiers, page numbers, token offsets, or structural coordinates.
At step 214, the system 100 may be configured to detect, by a semantic identity subsystem 116, entities within the contextual evidence units and generating deterministic entity identifiers corresponding to the detected entities using a cryptographic hashing function applied to a concatenated representation comprising a namespace identifier, an entity type classification, and a normalized entity representation.
At step 216, the system 100 may be configured to transform, by a canonical query abstraction subsystem 118, the natural language query into a structured semantic query representation comprising a domain concept, one or more attribute constraints, one or more relational predicates, and optionally numerical or aggregation constraints.
At step 218, the system 100 may be configured to validate, using a semantic binding registry 120, the structured semantic query representation by verifying correspondence with at least one mapping between conceptual entities and data source structures.
At step 220, the system 100 may be configured to retrieve, by a retrieval subsystem 104, a plurality of evidence items corresponding to the structured semantic query representation from at least two heterogeneous information sources, wherein retrieving comprises dynamically selecting a retrieval strategy and performing at least one of vector similarity search, structured At step 222, the system 100 may be configured to execute query, graph traversal, or document evidence retrieval, and aggregating the retrieved evidence items into a unified evidence set associated with deterministic entity identifiers and contextual evidence references.
At step 224, the system 100 may be configured to evaluating, by a reasoning validation subsystem 106, semantic compatibility among the plurality of evidence items based on at least one of shared deterministic entity identifiers, shared or hierarchically related ontology concepts, or alignment of query constraints, and computing a semantic overlap score using a weighted similarity function including a weighted Jaccard similarity measure, and determining that semantic compatibility is satisfied when the semantic overlap score exceeds a predefined validation threshold.
At step 226, the system 100 may be configured to control, by a synthesis control gate 110 operatively coupled to the reasoning validation subsystem 106, response generation by: (i) permitting generation of the grounded response only when the semantic compatibility is satisfied; (ii) restricting response generation to utilize exclusively validated evidence items; and (iii) preventing generation of the grounded response when semantic compatibility is not satisfied.
At step 228, the system 100 may be configured to generating, by a response generation subsystem 108, the grounded response using only the validated evidence items, wherein the grounded response includes references to one or more contextual evidence units associated with the validated evidence items and logging at least one of validation outcomes, semantic overlap scores, or synthesis control decisions to enable deterministic, auditable, and reproducible response generation. 
, Claims:I/We claim(s)
1. A system (100) for generating a grounded response to a natural language query using heterogeneous information sources, the system (100) comprises:
a user device (102) configured to receive one or more inputs representing a natural language query from a user; and
a server (103) comprises:
a retrieval subsystem (104) configured to obtain a plurality of evidence items corresponding to the natural language query from at least two heterogeneous information sources;
a reasoning validation subsystem (106) configured to evaluate semantic compatibility among the plurality of evidence items based on one or more compatibility conditions;
a response generation subsystem (108) configured to generate the grounded response; and
a synthesis control gate (110) operatively coupled between the reasoning validation subsystem (106) and the response generation subsystem (110), wherein the synthesis control gate (110) is configured to:
permit generation of the grounded response only when the reasoning validation subsystem (106) determines that the plurality of evidence items satisfy the semantic compatibility conditions;
restrict the response generation subsystem (108) to utilize exclusively validated evidence items that satisfy the semantic compatibility conditions; and
prevent generation of the grounded response when the semantic compatibility conditions are not satisfied, thereby enforcing deterministic, pre-synthesis validation-controlled response generation and preventing synthesis from semantically incompatible evidence sources, wherein the reasoning validation subsystem is configured to control response generation by permitting synthesis only when semantic consistency conditions are satisfied, and wherein the context reconstruction subsystem, semantic identity subsystem, semantic memory subsystem, canonical query abstraction subsystem, retrieval subsystem, and reasoning validation subsystem operate as coordinated layers of a deterministic knowledge processing architecture
2. The system (100) as claimed in claim 1, further comprises a semantic memory subsystem (114) that is communicatively coupled with each of a retrieval subsystem (104), a reasoning validation subsystem (106), a response generation subsystem (108), synthesis control gate (110), and configured to store entities, relationships, contextual attributes, and evidence references derived from the contextual evidence units, wherein the semantic memory subsystem (114) stores entity nodes comprising graph relationships, vector embeddings, contextual attributes, and references to source evidence.
3. The system (100) as claimed in claim 1, further comprises a context reconstruction subsystem (112) configured to process source information obtained from one or more data repositories, wherein the context reconstruction subsystem (112) is configured to segment the source information into a plurality of content blocks corresponding to structural elements including at least one of headings, paragraphs, tables, lists, or metadata fields, and to construct contextual evidence units representing semantically associated information.
4. The system (100) as claimed in claim 3, wherein the context reconstruction subsystem (112) is configured to:
extract semantic anchors from each content block, the semantic anchors comprising at least one of named entities identified using named entity recognition, ontology-defined conceptual terms identified using ontology matching, or domain-specific identifiers extracted from structured or semi-structured data;
determine an anchor overlap score between two or more content blocks based on a ratio of intersecting semantic anchors to a union of semantic anchors associated with the content blocks;
determine a positional proximity value representing a structural or token-based distance between the content blocks within an original source document;
merge the two or more content blocks into a contextual evidence unit when (i) the anchor overlap score exceeds a predefined anchor threshold and (ii) the positional proximity value is below a predefined positional gap threshold; and
associate each contextual evidence unit with positional metadata including at least one of document identifiers, page numbers, token offsets, or structural coordinates corresponding to original source locations.
5. The system (100) as claimed in claim 1, further comprising a semantic identity subsystem (116) configured to detect entities within contextual evidence units and generate deterministic entity identifiers corresponding to the detected entities, wherein the deterministic entity identifiers are generated using a cryptographic hashing function applied to a concatenated representation comprising: (i) a namespace identifier corresponding to a source modality, (ii) an entity type classification, and (iii) a normalized entity representation, wherein the namespace identifier is configured to distinguish entities originating from at least one of structured databases, unstructured documents, or ontology repositories to prevent cross-modal identity collisions, wherein the normalized entity representation is generated by applying at least one normalization operation including case normalization, lemmatization, punctuation removal, abbreviation expansion, or canonicalization; and wherein the semantic identity subsystem (116) is further configured to merge entity attributes and associated evidence references when identical deterministic entity identifiers are detected, and to maintain separate entity representations when namespace identifiers differ.
6. The system (100) as claimed in claim 1, further comprises a canonical query abstraction subsystem (118) that is configured to interpret the natural language query and transform the natural language query into a structured semantic query representation, wherein the structured semantic query representation comprises: (i) a domain concept, (ii) one or more attribute constraints, (iii) one or more relational predicates, and (iv) optionally, numerical or aggregation constraints.
7. The system (100) as claimed in claim 1, further comprises a semantic binding registry (120) configured to store mappings between conceptual entities of the structured semantic query representation and corresponding data source structures such that the canonical query abstraction subsystem (118) is further configured to validate the structured semantic query representation by verifying correspondence with at least one mapping stored in the semantic binding registry prior to execution.
8. The system (100) as claimed in claim 1, wherein the retrieval subsystem (104) is configured to retrieve evidence items from at least two heterogeneous information sources selected from document repositories, relational databases, knowledge graphs, and vector embedding indexes, wherein the retrieval subsystem (104) is further configured to dynamically select a retrieval strategy based on characteristics of the structured semantic query representation, further wherein the retrieval subsystem (104) is configured to perform at least one of: (i) vector similarity search, (ii) structured query execution, (iii) graph traversal, or (iv) document evidence retrieval; and wherein the retrieval subsystem (104) is further configured to aggregate retrieved evidence items into a unified evidence set, wherein each evidence item is associated with at least one deterministic entity identifier and a contextual evidence reference.
9. The system (100) as claimed in claim 1, wherein the reasoning validation subsystem (106) is configured to evaluate semantic compatibility among retrieved evidence items based on at least one of: (i) shared deterministic entity identifiers, (ii) shared or hierarchically related ontology concepts using ontology subsumption relationships, or (iii) alignment of query constraints associated with the retrieved evidence items, further wherein the reasoning validation subsystem (106) is further configured to compute a semantic overlap score using a weighted similarity function, further wherein the weighted similarity function comprises a weighted Jaccard similarity measure based on at least one of entity overlap, concept overlap, and constraint compatibility; and further wherein the reasoning validation subsystem (106) is configured to determine that the semantic compatibility is satisfied when the semantic overlap score exceeds a predefined validation threshold.
10. The system (100) as claimed in claim 1, wherein the synthesis control gate (110) is configured to: (i) block transmission of evidence items to the response generation subsystem (106) when the semantic compatibility conditions are not satisfied, (ii) selectively pass only validated evidence items satisfying the semantic compatibility conditions to the response generation subsystem (106), and (iii) enforce deterministic control over response generation by restricting input to the response generation subsystem exclusively to the validated evidence items.
11. The system (100) as claimed in claim 1, wherein the response generation subsystem (106) is configured to generate the grounded response using only the validated evidence items received through the synthesis control gate, and further wherein the grounded response includes references to one or more contextual evidence units associated with the validated evidence items.
12. A method (200) for generating a grounded response to a natural language query using heterogeneous information sources, the method (200) further includes:
receiving (202), at the user device (102), one or more inputs representing a natural language query from a user;
processing (204), by the context reconstruction subsystem (112), source information obtained from one or more data repositories by segmenting the source information into a plurality of content blocks corresponding to structural elements including at least one of headings, paragraphs, tables, lists, or metadata fields,
extracting (206), by the context reconstruction subsystem (112), semantic anchors from each content block, the semantic anchors comprising at least one of named entities, ontology-defined conceptual terms, or domain-specific identifiers;
determining (208), by the context reconstruction subsystem (112), an anchor overlap score between two or more content blocks based on a ratio of intersecting semantic anchors to a union of semantic anchors;
determining (210) a positional proximity value representing a structural or token-based distance between the content blocks;
merging (212), by a context reconstruction subsystem (112), the two or more content blocks into contextual evidence units when the anchor overlap score exceeds a predefined threshold and the positional proximity value is below a predefined threshold and associating each contextual evidence unit with positional metadata including at least one of document identifiers, page numbers, token offsets, or structural coordinates;
detecting (214), by a semantic identity subsystem (116), entities within the contextual evidence units and generating deterministic entity identifiers corresponding to the detected entities using a cryptographic hashing function applied to a concatenated representation comprising a namespace identifier, an entity type classification, and a normalized entity representation;
transforming (216), by a canonical query abstraction subsystem (118), the natural language query into a structured semantic query representation comprising a domain concept, one or more attribute constraints, one or more relational predicates, and optionally numerical or aggregation constraints;
validating (218), using a semantic binding registry (120), the structured semantic query representation by verifying correspondence with at least one mapping between conceptual entities and data source structures;
retrieving (220), by a retrieval subsystem (104), a plurality of evidence items corresponding to the structured semantic query representation from at least two heterogeneous information sources, wherein retrieving comprises dynamically selecting a retrieval strategy and performing at least one of vector similarity search, structured query execution, graph traversal, or document evidence retrieval, and aggregating the retrieved evidence items into a unified evidence set associated with deterministic entity identifiers and contextual evidence references;
evaluating (222), by a reasoning validation subsystem (106), semantic compatibility among the plurality of evidence items based on at least one of shared deterministic entity identifiers, shared or hierarchically related ontology concepts, or alignment of query constraints, and computing a semantic overlap score using a weighted similarity function including a weighted Jaccard similarity measure, and determining that semantic compatibility is satisfied when the semantic overlap score exceeds a predefined validation threshold;
controlling (224), by a synthesis control gate (110) operatively coupled to the reasoning validation subsystem (106), response generation by: (i) permitting generation of the grounded response only when the semantic compatibility is satisfied; (ii) restricting response generation to utilize exclusively validated evidence items; and (iii) preventing generation of the grounded response when semantic compatibility is not satisfied; and
generating (226), by a response generation subsystem (108), the grounded response using only the validated evidence items, wherein the grounded response includes references to one or more contextual evidence units associated with the validated evidence items and logging at least one of validation outcomes, semantic overlap scores, or synthesis control decisions to enable deterministic, auditable, and reproducible response generation.

Dated this 13th Day of April 2026

Prasad Prabhakar Karhad
IN/PA-2352
Agent for Applicant